EXECUTIVESUMMARY
Artificial intelligence (AI) is increasingly integrated across industries, reshaping how work is performed and how employers define skill requirements While recent attention has focused on generative AI tools, AI adoption in the labor market extends more broadly to technologies such as machine learning, data analytics, automation, predictive modeling, and AI-enabled decision support systems As a result, AI-related skills are becoming a common component of work across many occupations rather than a specialized or isolated capability
Labor market data show that demand for AI-related skills has grown steadily across industries and is now evident in a wide range of roles beyond traditional technology occupations, including production, healthcare, finance, logistics, and professional services This shift highlights the need for workforce and education systems to prepare workers for AI-enabled tasks across multiple sectors and education levels
The analysis confirms these trends within the regional economy, noting that industry adoption is most heavily concentrated in sectors such as Professional, Scientific, and Technical Services as well as Manufacturing On a granular level, sub-sectors like Motor Vehicle Parts Manufacturing and Employment Placement Agencies are leading the way with the highest volume of unique AI-related job postings While the most intense demand for these skills is currently found in roles requiring a bachelor’s degree, the associate degree remains an essential pathway for technical implementation and support roles
A significant skills gap has emerged locally, with AI competencies appearing in approximately 34% of relevant job postings, yet these skills are listed on significantly fewer worker profiles Current data suggest that AI is primarily acting to augment work by reallocating routine tasks, rather than causing widespread structural unemployment However, this shift has also notably raised the skill threshold for entry-level positions across nearly every sector
To address the broadening definition of AI in the workforce, the report identifies two distinct tiers of skills First, specialized technical skills such as Robotic Systems, Automation, Machine Learning, and Python are seeing high growth for associate-level roles Second, foundational human competencies that remain not automatable such as effective communication, complex troubleshooting, critical thinking, and ethical reasoning have become increasingly critical as AI-enabled decision support systems become common in the workplace
Given that AI is no longer an isolated capability, AI literacy should be treated as a foundational learning outcome rather than as a specialized elective Curriculum design must emphasize human-AI collaboration, teaching students to use AI tools while applying professional judgment to validate and improve the resulting outputs Instructional models should prioritize applied capability by utilizing project-based learning and real-world scenarios that mirror workplace practices Responsible AI use, including data security, privacy, and bias mitigation, must be embedded into coursework to ensure graduates are prepared for the ethical complexities of an AI-integrated workforce
While some uncertainty is to be expected, AI adoption presents a significant opportunity for the college to normalize AI skills as a component of professional life Success in this new landscape requires a balanced approach that pairs innovative technical training with a reinforced focus on the essential human judgment that AI cannot replicate
ArtificialIntelligence (AI)Impact Assessment
PHASE 1: Research and Data Analysis
PHASE1:RESEARCHAND DATAANALYSIS
INTRODUCTION
Since November 2022, Generative Artificial Intelligence (GAI) has rapidly entered public awareness and organizational practice, reshaping conversations about work, education, and skill development (Wong, 2024). Advances in large language models have accelerated this shift, with later versions of tools such as ChatGPT demonstrating capabilities that rival or exceed human performance on complex tasks.
One notable example of this performance is scoring within the top 10 percent of candidates on the U.S. Uniform Bar Exam, an outcome that contrasts sharply with earlier GPT models that barely met passing thresholds (Wong, 2024). These advances underscore both the promise and uncertainty of AI’s expanding role across society.
As AI technologies become more embedded in economic and educational systems, they function as a form of large-scale social experimentation, introducing profound changes whose long-term impacts are not yet fully understood (Hosseini et al, 2023) Existing research suggests that AI adoption is already reshaping workforce dynamics by shifting demand toward more educated and technically skilled workers (Babina & Fedyk, 2025)
However, historical patterns indicate that technological change tends to create new forms of employment over time rather than produce sustained structural unemployment (Briggs & Kodnani, 2023) This duality suggests the possibility of disparate impact among workers and highlights the need for deliberate, evidencebased responses, particularly within education Given the double-edged nature of GAI in learning environments, educators and workforce institutions face the challenge of integrating AI thoughtfully, balancing innovation with ethical considerations, and avoiding resistance that could inadvertently hinder necessary skill development (Wong, 2024)
In response to these shifts, Phase 1 examines emerging AI-related skill demands across industries and education levels. Industries are categorized using the North American Industry Classification System (NAICS), a standard framework employed by U.S. federal statistical agencies to ensure consistency and comparability in economic and workforce analysis. By integrating industry classifications with job posting data, education-level requirements, and regional, state, and national comparisons, this assessment provides actionable insights for community colleges seeking to align programs with employer needs. The findings support curriculum alignment, program planning, and talent development strategies to prepare learners for an economy in which AI skills are increasingly normalized and essential across industries.
IndustryDefinitionsandClassificationLevels
In this analysis, an industry is defined as a group of establishments engaged in similar economic activities. This classification framework supports consistent analysis of employment trends, output and skill needs.
The two-digit NAICS level offers a high-level view of AI-related demand across broad sectors.
The six-digit NAICS level provides a detailed insight into specific industries where AI competencies are most concentrated or emerging
Together, these levels connect macro-level industry patterns with skill needs that provide for workforce planning, program development, and curriculum alignment
AIDEPLOYMENTBYINDUSTRY
2-DigitIndustryDefinitions
1.Professional, Scientific, and Technical Services: Includes establishments that specialize in performing professional, scientific, and technical activities for others. Examples include legal services, accounting, architecture, engineering, computer system design, and R&D.
2.Manufacturing: Industries engaged in the mechanical, physical, or chemical transformation of materials, substances, or components into new products. This sector includes factories and production facilities that produce goods from raw materials or parts.
3 Administrative and Support and Waste Management and Remediation Services: Includes establishments that perform routine support activities for the day-to-day operations of other organizations Activities include office administration, facilities support, employment services, and waste management/remediation
4 Information: Consists of establishments engaged in producing and distributing information and cultural products, providing the means to transmit or distribute these products, or processing data This sector Includes publishing, motion picture and sound recording, broadcasting, telecommunications, and data processing
5 Health Care and Social Assistance: Includes establishments that provide health care and social assistance for individuals Covered activities include hospitals, nursing care, outpatient services, social services, and childcare
6.Finance and Insurance: Comprises establishments primarily engaged in financial transactions including creation, liquidation, or transfer of financial assets, as well as activities that facilitate financial intermediation. This sector covers banks, credit institutions, investment firms, and insurance companies.
7.Retail Trade: Industries that sell merchandise in small quantities to the general public for personal or household consumption. Establishments typically operate through physical storefronts or online platforms and include businesses such as clothing and electronics retailers, grocery stores, vehicle dealers, and e-commerce providers.
AIDEPLOYMENTBYINDUSTRY
The six-digit NAICS level is used to examine detailed industry-level AI skill requirements, enabling identification of specific industries with concentrated or emerging demand for AI-related competencies. This granular classification supports actionable insights for workforce development strategies, curriculum alignment, and program planning at the community college level.
Together, the two- and six-digit NAICS classifications connect broad industry trends with specific AI skill needs, ensuring that study findings remain directly applicable to educational planning, workforce training, and regional talent development
Figure1.2.Top6-DigitIndustrieswithAIbyUniquePostings
Administrative
Custom Computer Programming Services
of Certified Public Accountants
AISKILLSBYEDUCATIONALATTAINMENT
Figure 1.3 compares trends in job postings requiring AI-related skills by education level. Across all education categories, the data display a consistent pattern: job postings peak in 2018, decline in 2020, rebound in 2022, and decrease thereafter. This trajectory closely mirrors overall job posting trends, indicating that fluctuations in AI-related hiring largely reflect broader labor market dynamics, while also suggesting that the two may not yet be closely correlated.
When disaggregated by education requirement, job postings requiring a bachelor’s degree account for the largest share of AI-related demand, reaching a high of 30,109 postings. Positions requiring a master’s degree follow, with 15,628 postings in 2022 In contrast, postings requiring a high school diploma or GED and those requiring an associate degree represent the smallest share of AI-related demand, with peak values of 1,958 and 1,613 postings, respectively These patterns may reflect the lower volume of jobs that require an advanced degree in the broader labor market
Overall, the distribution highlights that AI-related job demand is concentrated in roles requiring higher levels of formal education, while positions with lower educational requirements comprise a relatively small portion of AI-skilled labor demand This pattern stresses the importance of postsecondary education pathways in preparing workers for AI-related roles
AISKILLSBYEDUCATIONALATTAINMENT
Figure 1.4 provides a more detailed examination of job postings filtered by educational requirement, focusing on AI-related positions that specify an associate degree as the required level of education. This analysis highlights the most frequently requested specialized skills within these postings. Each skill is analyzed according to the number of job postings in which it appears, along with its projected growth rate, offering insight into both current employer demand and anticipated future relevance.
By examining these skills in tandem, the findings identify competencies that are most commonly sought in associate-level AI roles as well as projected to experience the strongest growth over time
Figure1.4.TopSpecializedSkillsfortheAIIndustryinAssociate’sDegreePostings
Artificial
Programmable
Project Management
This dual perspective distinguishes skills that are already well established in the labor market from those emerging as increasingly important for entry- and mid-level AI-related positions
Overall, the results illustrate the specific technical and applied skills that define AI career pathways at the associate degree level, offering valuable guidance for workforce development programs, community colleges, and training providers seeking to align curricula with employer needs
AISKILLSBYEDUCATIONALATTAINMENT
Figure1.5.TopCommonSkillsfortheAIIndustryinAssociate’sDegreePostings
Next, we examine the most frequently cited common skills in AI-related job postings that specify an associate’s degree as the required level of education. Each skill is presented alongside the number of postings in which it appears and its projected growth rate, offering insight into both current demand and anticipated future importance
By focusing on common skills, the next section highlights foundational and transferable competencies that are widely required across AI-related roles at the associate degree level Comparing posting frequency with projected growth helps identify skills that represent core labor market expectations from those becoming increasingly critical as AI technologies continue to evolve
AISKILLSBYEDUCATIONALATTAINMENT
Figure1.6.TopSpecializedSkillsfortheAIIndustryinBachelor’sDegreePostings
Computer Science
Python (Programming Language)
Electrical Engineering
Machine Learning
Advanced Driver Assistance Systems
Project Management
Artificial Intelligence
Automation
Software Development
Data Analysis
SQL (Programming Language)
Agile Methodology
Computer Engineering
New Product Development
Software Engineering
C++ (Programming Language)
Figure 1.6 analyzes the most frequently requested specialized skills in AI-related job postings that list a bachelor’s degree as the required level of education. Each skill is presented with the number of job postings in which it appears and its projected growth rate, offering insight into both current employer demand and anticipated future skill needs.
The results highlight advanced technical and analytical competencies that define AI-related roles at the bachelor’s degree level. By examining both posting frequency and projected growth, this analysis distinguishes between skills that are already well established in the labor market and those expected to become increasingly important as AI adoption expands across industries
AISKILLSBYEDUCATIONALATTAINMENT
Figure1.7.TopCommonSkillsfortheAIIndustryinBachelor’sDegreePostings
Figure 17 illustrates the most frequently cited common skills in AI-related job postings that require a bachelor’s degree Each skill is presented alongside the number of job postings in which it appears and its projected growth rate, providing a comprehensive view of both current demand and anticipated future relevance
AISKILLSGROWTHTIMELINE
Figure 1.8 illustrates changes in the number of job postings requiring AI-related skills in the MCC region from 2010 to 2025. Job postings increased steadily throughout the early and mid-2010s, reaching a peak of 32,152 postings in 2018. This growth reflects the expanding adoption of AI technologies across industries during that period.
Following this peak, postings declined sharply to 21,592 in 2020, coinciding with widespread labor market disruptions during the COVID-19 pandemic. In 2022, the number of AI-related job postings rebounded significantly to 43,349, surpassing pre-pandemic levels This surge mirrors a broader labor market pattern in which employers increased hiring activity to offset job losses experienced in 2020
After 2022, AI-related job postings declined again, falling to 16,947 by 2025. This downward trend may indicate market stabilization, increased efficiency through automation, or consolidation in AI hiring as organizations shift from rapid expansion to optimization.
Figure 1.9 presents the percentage of total job postings in the MCC region that require AI-related skills from 2010 to 2025. While the overall pattern closely mirrors trends observed in the absolute number of AI-related job postings, the share of postings requiring AI skills demonstrates a more sustained upward trajectory over time.
Although temporary fluctuations are visible, particularly around 2020, the percentage of job postings with AI requirements continues to increase across the period. By 2025, the proportion of postings requiring AI skills remains nearly equivalent to 2022 levels, despite a decline in the absolute number of postings This indicates that AI skills have become more deeply embedded across the labor market rather than concentrated in periods of high hiring volume
Figure1.9.ShareofJobPostingsRequiringAISkillsinthe MCCRegion(2010–2025)
Overall, the findings suggest that over time, AI competencies are required in an increasing share of jobs, even during periods of overall contraction in hiring This trend highlights the normalization of AI as a baseline skill requirement across industries in the MCC region and reinforces the growing importance of AI literacy and related capabilities within the workforce.
AISKILLSGROWTHTIMELINE
State-LevelComparison:Michigan
The State of Michigan exhibits trends in AI-related job postings that closely parallel those observed in the MCC region The total number of job postings requiring AI skills peaked in 2022 at 72,608 postings, followed by a decline to 34,573 postings by 2025 This pattern reflects broader fluctuations in overall labor demand rather than a reversal in AI adoption.
When examined as a share of total job postings, however, AI requirements continue to demonstrate an upward trend. The percentage of postings
Figure1.10.TrendsinJobPostingsRequiringAISkillsin theStateofMichigan(2010–2025)
requiring AI skills reached a high of 17% in 2022, with 2025 nearly matching this level at 16%, despite the decrease in absolute posting volume This indicates that AI skills remain consistently embedded in employer demand across the state
Figure1.11.ShareofJobPostingsRequiringAISkillsin theStateofMichigan(2010–2025)
Taken together, the findings suggest that while hiring volumes fluctuate over time, the relative importance of AI competencies within Michigan’s labor market continues to grow. Similar to the MCC region, AI skills are increasingly incorporated into a broad range of roles, marking their role as a persistent and structural component of workforce demand rather than a temporary hiring trend
National-LevelComparison
At the national level, job postings requiring AI-related skills follow patterns similar to those observed at the local and state levels, while exhibiting a more pronounced long-term upward trend The number of AIrelated job postings increased substantially through 2022, reaching 2,298,394 postings, before declining in 2023, consistent with broader labor market adjustments seen at the regional and state levels Following this dip, AI-related postings rose again through 2025, totaling 1,851,969 postings
When evaluated as a percentage of total job postings, the national data show an even stronger and more consistent upward trajectory than observed locally or statewide Over time, AI skills account for an increasing share of employer demand, reflecting their expanding integration across sectors and
AISKILLSGROWTHTIMELINE
occupations By 2025, 23% of all job postings nationwide require AIrelated skills, highlighting the growing normalization of AI competencies within the US labor market
Overall, the national trends reinforce findings at the MCC and Michigan levels: while absolute posting volumes fluctuate in response to economic conditions, the relative importance of AI skills continues to increase, signaling a structural shift in workforce requirements rather than a temporary hiring phenomenon.
Figure1.12.TrendsinJobPostingsRequiringAISkillsin
Figure1.13.ShareofJobPostingsRequiringAISkillsinthe
DEMAND:JOBPOSTINGSVERSUSPROFILES
Table1.TopSpecializedAISkills:JobPostingsVsWorkerProfiles
This section compares the most in-demand specialized skills in AI-related job postings with the percentage of workforce profiles that list the same skills, highlighting gaps between employer demand and available talent. By examining these two measures side by side, the analysis identifies areas where the workforce may be underprepared relative to labor market needs.
The results reveal substantial disparities between job requirements and skill representation among career seekers For example, artificial intelligence appears in 34% of AI-related job postings yet is listed in only 1% of career-seeker profiles The gap indicates a significant shortfall in AI-specific competencies within the current workforce
Overall, the findings underscore critical skill mismatches that may constrain AI adoption and workforce readiness Identifying these gaps provides actionable insights for education providers, training programs, employers, and policymakers seeking to strengthen talent pipelines and align workforce skills with employer demand.
DEMAND:JOBPOSTINGSVERSUSPROFILES
18% 8%
4,363 17% 2% Troubleshooting 4,183 17% 3%
3,773 15% 4%
3,554 14% 8%
3,497 14% 15%
13% 18%
3,268 13% 3%
13% 3%
Oriented 2,904 12% 1%
2,645 11% 10%
2,400 10% 1%
2,366 9% 2%
2,183 9% 12%
2,073 8% 3%
2,069 8% 1%
This section applies the same demand-versus-supply analysis to common skills in AI-related job postings, comparing employer requirements with the percentage of career-seeker profiles that list these competencies. While gaps persist, they are generally less pronounced than those observed for specialized skills, reflecting the broader and more transferable nature of common skills
The findings indicate that communication skills are required in 46% of AI-related job postings, yet appear in only 10% of worker profiles, highlighting a notable but narrower gap Similarly, management skills are listed in 27% of postings compared to 14% of profiles, suggesting a moderate shortfall in leadership-related competencies
Overall, this analysis demonstrates that although common skills are more widely represented in the workforce than specialized AI skills, meaningful mismatches remain. Addressing these gaps through targeted training and professional development may help strengthen workforce readiness and improve alignment between employer expectations and available talent in AI-related roles.
ArtificialIntelligence (AI)Impact Assessment
PHASE 2: AI Use by Occupation
PHASE2:AIUSEBY OCCUPATION
AIOCCUPATIONOVERVIEW
OccupationClassificationUsingSOC
An occupation is defined as a group of jobs that involve similar tasks, skills, and work activities, even when they span multiple industries. For example, an accountant may work in healthcare, finance, or construction but still perform the same core occupational functions. Occupations in this study are classified using the Standard Occupational Classification (SOC) system, the federal framework for organizing and comparing jobs across the US labor market
The two-digit SOC level is used to examine AI-related skill demand across broad occupational groups, such as Computer and Mathematical Occupations or Healthcare Practitioners This level provides a high-level view of where AI-related skills are most prevalent across major areas of work
The six-digit SOC level allows for detailed analysis of specific occupations, identifying individual roles with strong or emerging demand for AI-related skills Using both levels together provides insight into overall trends while supporting workforce planning, curriculum development, and alignment with community college training programs
Figure2.1.Top10Two-DigitOccupationsRequiringAIbyPostings
The following pages analyze each of the top 10 two-digit occupations, focusing on job postings that include AI to better understand the role of AI within each occupation Job postings from January 2023 through October 2025 in the MCC region are examined by leading occupations, average postings per quarter, and required education levels, specialized skills, and software skills This analysis supports a clearer understanding of career pathways and credential requirements, informing upskilling, tuition considerations, and potential barriers to entry In addition, the software and specialized skills highlight the tools used on the job, identify employable skills, and help make these occupations more tangible
10,180
Job Postings
Requiring AI Jan ‘23 - Oct ’25
The Computer and Mathematical Occupations group includes roles in computing, information technology, data analysis, and quantitative problem-solving These jobs involve applying technical, analytical, and mathematical skills to develop, manage, or analyze digital systems and data
There were 3,608 Software Developer job postings in the area, making it the most frequently posted occupation within Computer and Mathematical Occupations.
Job postings requiring AI skills have been gradually increasing since the decline in late 2023.
Most AI-related postings in this occupational group require a bachelor's degree, reinforcing the high technical skill expectations of these roles
AIUSEBYOCCUPATIONGROUP
3,560 Job Postings Requiring
Jan ‘23 - Oct ’25
The Architecture and Engineering Occupations group is involved in designing, planning, and oversight of the construction and operation of buildings, infrastructure, and technological systems These roles apply engineering, architectural, and scientific principles to develop efficient, safe, and innovative solutions
There were 708 Electrical Engineer job postings in the area, ranking it as the most common occupation within Architecture and Engineering Occupations
Job postings requiring AI skills have slowly rebounded since the decline in Q4 2023, increasing from 191 postings in Q4 2023 to 210 in Q3 2024
Across roles that require AI skills, a bachelor’s degree is the most common education requirement, with electrical engineering cited as the top specialized skill and Python as the most frequently requested software skill.
AIUSEBYOCCUPATIONGROUP
ManagementOccupations
2,694 Job Postings Requiring AI Jan ‘23 - Oct ’25
The Management Occupations Group includes roles responsible for planning, directing, and coordinating the operations of organizations or departments These jobs involve strategic decision-making, resource management, and oversight of people, and projects.
There were 606 Marketing Manager job postings in the area, making it the most frequently posted occupation within Management Occupations.
Job postings requiring AI skills have increased overall since Q1 2023, rising from 202 postings to 262 postings by Q3 2025.
Among positions that list AI requirements, a bachelor’s degree is the most common education level, with artificial intelligence identified as the leading specialized skill and Microsoft Suite as the most frequently requested software skill
AIUSEBYOCCUPATIONGROUP
2,506
The Business and Financial Operations Group involves supporting organizational efficiency through activities like financial analysis, budgeting, project management, and compliance. These roles apply analytical and administrative skills to help organizations operate effectively and make informed decisions.
Market Research Analyst and Marketing Specialist job postings accounted for 423 job postings in the region, making this the most frequently posted occupation within Business and Financial Operations Occupations
Postings fluctuated by
but remained steady overall.
Across positions that list AI requirements, a bachelor’s degree is most often required Employers most frequently identify artificial intelligence as the leading specialized skill, while proficiency in Microsoft Suite appears as the most commonly requested software skill
AIUSEBYOCCUPATIONGROUP
The Installation, Maintenance and Repair Occupations Group involves keeping equipment, machinery, and systems functioning safely and efficiently. These roles include installing, troubleshooting, maintaining, and repairing a wide range of mechanical, electrical, and technical equipment
Maintenance and Repair Workers, General positions accounted for 260 job postings in the region, making this role the most frequently posted occupation within this group
Postings varied by quarter but were stable overall.
Most AI-related roles require a high school diploma, followed by an associate degree, with Advanced Driver Assistance Systems as the top specialized skill and Microsoft Suite as the most frequently requested software skill
The Sales and Related Occupations Group involves selling products and services, building customer relationships, and providing assistance throughout the sales process These roles range from retail and wholesale sales to sales representatives, and other positions focused on meeting customer
Overall, job postings have increased steadily from Q1 2023 to Q3 2025, rising from 54 postings to 97, indicating growing demand over time Most positions that list AI requirements require a bachelor’s degree, with artificial intelligence identified as the leading specialized skill and Microsoft Office cited as the most frequently requested software skill
AIUSEBYOCCUPATIONGROUP
AI
‘23 - Oct ’25
The Arts, Design, Entertainment, Sports, and Media Occupations Group involves creative expression, performance, communication, and the design or production of visual, written, or audio content These roles support cultural, artistic, and informational activities across a wide range of industries
Commercial and Industrial Designers accounted for 266 job postings in the area, representing the most in-demand occupation within this group
Quarterly job postings have fluctuated, with only a slight overall decline since Q1 2023
Most roles that include AI requirements call for a bachelor’s degree, with artificial intelligence noted as the top specialized skill and Microsoft PowerPoint listed as the most frequently requested software skill.
AIUSEBYOCCUPATIONGROUP
The Office and Administrative Support Occupation Group involves clerical, organizational, and administrative tasks that help businesses operate efficiently These roles include duties such as recordkeeping, scheduling, customer support, and information management.
First-Line Supervisors of Office and Administrative Support Workers accounted for 106 job postings in the area, making it the most frequently posted occupation within this group.
Quarterly job postings have shown a modest overall increase since Q1 2023.
Positions that list AI requirements most often require a bachelor’s degree, with artificial intelligence identified as the leading specialized skill and Microsoft Suite as the most commonly requested software skill.
AIUSEBYOCCUPATIONGROUP
The Healthcare Practitioners and Technical Occupations Group includes professionals who diagnose, treat, and support patient care through clinical and technical expertise. These roles range from physicians and nurses to technologists and therapists who deliver essential medical services.
Registered Nurses accounted for 149 job postings in the area, making it the most frequently posted occupation within Healthcare Practitioners and Technical Occupations
job postings experienced a sharp increase in Q1 and Q2 2024, likely reflecting a broader surge
Among roles that list AI requirements, most do not specify an education level, with chatbot identified as both the leading specialized skill and the most frequently requested software skill
Postings Requiring
Jan ‘23 - Oct ’25
The Production Occupations Group involves operating, maintaining, and overseeing the machinery and processes used to manufacture goods These roles include assembling, fabricating, and inspecting products to ensure quality and efficiency in production environments.
Production Workers, All Other accounted for 105 job postings in the area, making them the most frequently posted occupation within Production Occupations.
Quarterly job postings declined noticeably in Q1 2024 but rebounded to nearly the same level by Q3 2025
Positions that list AI requirements most frequently require a high school diploma, with continuous improvement processes identified as the leading specialized skill and Microsoft Suite as the most commonly requested software skill.
AIUSEBY2-DIGITINDUSTRYNAICSCODE
2-DigitIndustries
This section analyzes the top two-digit industries identified in Phase 1, focusing on the presence of AIrelated skill requirements in job postings. The analysis compares postings that require AI skills with those that do not, highlighting differences in hiring patterns and illustrating how AI adoption varies across industries Top2-DigitIndustrieswithAIbyUniquePostings
PostingsRequiringAISkills
AllPostings
$72,064
ArtificialIntelligence (AI)Impact Assessment
PHASE 3: Interviews with Industry Representatives
PHASE3:INTERVIEWSWITH INDUSTRYREPRESENTATIVES
LESSONSLEARNED
Introduction
WIN conducted four semi-structured interviews with professionals across diverse occupations who regularly use AI in their day-to-day responsibilities and broader organizational workflows Participants were purposively selected based on their direct, sustained experience with AI-enabled tools and systems Each interview followed a standardized interview protocol consisting of thirteen core questions, with follow-up probes employed as needed to clarify responses or explore emergent issues The interview protocol was structured around four analytic domains: Context and Strategy; Impact on Jobs and Roles; Critical Skill Gaps; and Education and Workforce Preparedness Interview data were analyzed using a thematic analysis approach, with recurring themes identified through iterative review These themes were then examined alongside existing empirical and conceptual literature to assess areas of convergence and divergence between practitioner perspectives and established research on AI adoption, workforce impacts, and skill development.
Context and Strategy
Impact on Jobs and Roles
Critical Skill Gaps
Education and Workforce Preparedness
Theme1:AIDiffusionAcrossIndustriesandOccupationsorAcceleratingand WidespreadAdoptionofArtificialIntelligence
AI adoption is described as accelerating and increasingly widespread across industries Recent research indicates that firms’ investments in AI are driven less by cost-cutting or workforce reduction and more by productivity and revenue growth, particularly through sales and operational improvements (Brookings, 2025) All four interviewees report meaningful AI use within their organizational settings, ranging from early-stage exploration to deeply embedded systems The findings align with real-time evidence from the Business Trends and Outlook Survey (BTOS), which shows firm-level AI use rising from 37% to 54% between September 2023 and February 2024, with expected use reaching approximately 6.6% by early Fall 2024 (Bonney et al., 2024). Employment-weighted exposure is even higher, increasing from roughly 4.5% to nearly 9%, with projected growth to between 10% to 12% (Bonney et al., 2024).
LESSONSLEARNED
Employer demand for AI-related skills increased from approximately 05% of job postings in 2010 to 17% in 2024, representing a 240% increase (Galeano et al, 2025), with nearly 628,000 postings requiring at least one AI skill in 2024 alone (Galeano et al, 2025) This finding is further supported by data from the Macomb Community College service region, where job postings requiring AI-related skills increased from 05% in 2010 to 16% in 2024 and 20% in 2025 This data and associated chart are shown on page eleven While this increase mirrors the findings by Galeano et al. (2025), the data suggests that this region has even greater demand than the baseline comparison. AI-driven transformation spans both white-collar and blue-collar occupations, as roles built around clearly defined and formulaic procedures are particularly susceptible to automation. Across contexts ranging from operating theatre or modern farming operations, work reliant on standardized protocols can be more readily replicated by AI systems (Wong, 2024).
AI in almost every aspect of our business… probably 25 to 30 different AI platforms… everything from generating emails and onboarding programs to predictive modeling and sales strategy” (Kirchner, 2025) and “We are sitting both sides of the house in terms of AI-the development of it in real time as well as how we are using it organizationally” (Bartos, 2025) Sectoral variation documented in the literature, with adoption highest in information intensive industries and lowest in construction and agriculture, supports these interview findings and indicates that although adoption levels differ across sectors, AI use has moved beyond experimentation and is becoming increasingly mainstream across industries (Bonney et al, 2024)
Theme2:AIisPrimarilyAugmentingandNotReplacingtheWorkforce
All four interviewees and the literature emphasize that AI is primarily augmenting, rather than replacing, the workforce. While some routine and repetitive tasks are increasingly subject to automation, large-scale job elimination is not the dominant outcome. Interviewees consistently reinforced this perspective. As one interviewee stated, “We have no intention to eliminate roles. We’re very focused on augmenting the existing workforce… we’ve almost doubled the size of our workforce since adopting AI” (Kirchner, 2025). Another interviewee similarly noted that “Most people are looking at ways to support or augment their work” emphasizing that augmentation rather than replacement is the prevailing approach (Roethele, 2025). Patrick further distinguished task automation from job loss, stating, “Anything repetitious will be automated but that’s different from eliminating all the jobs It’s about streamlining operations” (Dicks, 2025)
Empirical evidence supports this augmentation focused pattern Data from the Business Trends and Outlook Survey show that 946% of AI-using firms reporting no change in employment levels, while only 26% reported employment declines (Bonney et al, 2024) In addition, firms that reported task replacement typically replaced only a small number of tasks rather than entire roles (Bonney et al, 2024) At the same time, the literature acknowledges uneven workforce impacts, particularly for younger workers and entry-level roles in highly exposed occupations This is further supported by examining a breakdown of AI-related skills demand from employers by education level
LESSONSLEARNED
As shown on page seven, job postings which require a bachelor’s or master’s degree have the highest occurrence rates of AI skills being mentioned. In these contexts, employment declines have occurred as AI automates specific tasks with an estimation of 40 percent of employers planning workforce reductions related to automation (Brynjolfsson et al., 2025; World Economic Forum, 2025).
“Most people are looking at ways to support or augment their work.”
-Jody Roethele
Theme3:RisingAISkillExpectationsforEntry-LevelWorkers
A strong and consistent theme across interviews is the growing expectation for AI skills among new graduates and entry-level workers. All four interviewees agree that AI literacy is no longer optional and should be considered a baseline requirement. Interviewees emphasized that new entrants are increasingly expected to arrive with practical AI competencies rather than develop those skills on the job. As one interviewee noted, “New graduates are expected to come in with a higher level of AI proficiency understanding edge-to-cloud is critical” (Kirchner, 2025) and “AI proficiency is now the expectation, not the exception Entry-level workers should already understand things like prompting and agent creation” (Bartos, 2025) AI skill demand has broadened across all education levels, including associate degree and certificate-based occupations, demonstrating that AI literacy is no longer confined to advanced-degree roles (Galeano et al, 2025)
At the same time, interviews and secondary sources highlight structural challenges for early career workers As AI becomes more embedded in workplace processes, entry level job opportunities are declining and skill thresholds for entry are rising Patrick highlighted this shift by stating, “Entry-level jobs are shrinking. Young adults have fewer opportunities, and that means the bar is higher coming in.” (Dicks, 2025). Importantly, as AI becomes more embedded in the workplace, entry-level opportunities are declining, traditional career pathways, especially in whitecollar fields, are being reshaped, and compensation expectations are adjusting downward (World Economic Forum, 2025).
“Entry-level jobs are shrinking. Young adults have fewer opportunities, and that means the bar is higher coming in.”
-Dr. Dicks
Theme4:CriticalThinkingasaCoreWorkforceSkillinanAIDrivenWorkplace
Beyond technical proficiency, interviewees unanimously emphasize critical thinking as a core, nonautomatable skill in an AI-driven workplace. Critical thinking includes fact-checking, ethical reasoning, and the ability to evaluate AI outputs. Brooke underscores the importance of this capability by stating, “Critical thinking is absolutely necessary knowing how to tease out whether outputs are true or hallucinations is huge” (Bartos, 2025), while Patrick adds, “Technology cannot reason ethically Critical thinking is very hard to automate” (Dicks, 2025) These interview insights align with findings from the Macomb Community College Regional Industry IT Skill Needs Assessment which identified critical thinking and analysis as the most important emerging skill for the next five years (An etal, 2024)
LESSONSLEARNED
Figure3.1.Whichemergingskillsdoyouconsiderasthemostimportantforthenext fiveyears?
Critical thinking and analysis
Reasoning, problem-solving and ideation
Analytical thinking and innovation
Resilience, stress tolerance and flexibility
Complex Problem-solving
Creativity, originality and initiative
Emotional Intelligence
Active Learning and learning strategies
and control
Interestingly, these statements stand in contrast to the most recent job postings data for Artificial Intelligence-related jobs. Between January 2023 and October 2025, only 5% of these job postings mentioned Critical Thinking as a “Top Common Skill,” placing it as the 35 most posted common skill and well below the tenth most posted common skill, Sales th
However, Critical Thinking is the eighth highest skill when sorted by “fastest growing” among the same data and is characterized as “Rapidly Growing” relative to the market
Figure3.2.TopPostedCommonSkillsinArtificial IntelligenceRelatedJobs
LESSONSLEARNED
The broader literature further cautions that increased reliance on AI driven decision making may constrain employee learning and skill development When algorithmic systems automate decisions or obscure how outcomes are generated, opportunities for workers to learn from mistakes and refine judgment may be reduced. Limited transparency in algorithmic processes further constrains employee understanding and development, reinforcing the importance of critical thinking as a complementary human capability in AI enabled work environments (Hosseini et al., 2023).
Theme5:AdaptabilityandLearningAgility
Adaptability, curiosity, and a willingness to experiment also emerge as essential workforce traits across interviews. Interviewees repeatedly highlight the value of workers who are willing to learn, test new tools, and evolve alongside rapidly changing technologies. As Matt explains, “We want people who are fearless to explore people who are curious and take initiative” (Kirchner, 2025), while Brooke notes, “There will be somebody behind you that is curious, willing to learn, willing to experiment, and wanting to be on the forefront of what’s next.” (Bartos, 2025). Patrick reinforces the importance of adaptability by stating, “The biggest thing is adaptability understanding how things work and being open to learning as technology changes.” (Dicks, 2025). These perspectives align with evidence that many firms are still experimenting with AI, with over half reporting no organizational changes yet, indicating an ongoing learning and adjustment phase (Bonney et al, 2024) Demand for AI-related skills varies widely by occupation and education level, underscoring the importance of adaptability as AI expands beyond traditional technology roles (Galeano et al, 2025) Figure3.3.TopPostedCommonSkillsRankedbyFastestGrowing
“We want people who are fearless to explore— people who are curious and take initiative.”
-Matt Kirchner
LESSONSLEARNED
Theme6:Security,Privacy,andEthicalUseofArtificialIntelligence
“Most models are built around a white, Western male lens… understanding where biases come into play and how to combat them is big.”
-Brooke Bartos
Security, privacy, and ethical use of artificial intelligence emerged as a consistent theme across all interviews Interviewees raised concerns about data security, ethical reasoning, and bias mitigation, particularly in the context of integrating AI tools into organizational workflows These concerns align closely with the broader literature, which highlights risks such as hallucinations, embedded bias, and ethical challenges associated with large language models and generative AI systems (Wong, 2024) Issues related to bias, privacy, and data protection were echoed throughout both interview findings and existing research As Brooke noted, “Most models are built around a white, Western male lens… understanding where biases come into play and how to combat them is big.” (Bartos, 2025).
Interviewees also cautioned against uncritical reliance on AI generated outputs and emphasized the importance of human oversight. Jody emphasized the need for human oversight, stating, “It’s like having an intern you have to proof everything that comes out of AI. We never just run something through AI and send it out.” (Roethele, 2025), while Matt reinforced the importance of safeguards by noting, “Understanding privacy and data protection is fundamental” (Kirchner, 2025) These interview insights are reflected in firm-reported barriers to AI adoption, with privacy, security, and bias cited as ongoing challenges (Bonney et al, 2024)
Theme7:TheNeedforUpskillingandReskillinginanAI-DrivenWorkforce
Both the interviews and the literature underscore the need for a combined approach to upskilling and reskilling as AI continues to spread across occupations Interviewees recognize that while some workers will expand and deepen skills within their current roles, while others will need to transition into new positions as job requirements evolve Brooke captures this dual challenge by stating, “Upskilling is going to be necessary for everyone just to keep their jobs but there’s also a big opportunity for reskilling into new roles” (Bartos, 2025). Jody and Patrick reinforce this perspective by pointing to organizational constraints and resistance to change that can complicate these efforts (Roethele, 2025; Dicks, 2025).
The literature similarly highlights the importance of both strategies The growing demand for AI skills and in high school and associate degree occupations signals a need to upskill incumbent workers, particularly those trained through certificates and associate degree programs At the same time, the concentration of high-growth AI demand within Computer and Mathematical occupations indicates that reskilling pathways into technical roles remain essential (Galeano et al, 2025) Despite the potential for AI to displace some roles, existing research also suggests that AI adoption may expand job access and reshape the labor market through greater emphasis on upskilling and alternative training models such as apprenticeships (World Economic Forum, 2025) These approaches are especially important as employers anticipate workforce reductions due to automation and as global competition intensifies, shaping both salary expectations and job market dynamics for younger workers (Briggs & Kodnani, 2023)
LESSONSLEARNED
Theme8:TechnicalFoundationsSupportingAIAdoptionandWorkforceChange
The final theme emerging from both the interviews and the literature is the importance of strong technical foundations. While specific technical needs vary by role, there is a shared emphasis on core digital skills such as Python, AI agent development, cloud fundamentals, and data security awareness. Research shows that AI adoption is shifting employment toward more educated and technically skilled workers, with AI-related skill often associated with higher wages (Babina et al., 2024). At the same time, AI skills often command higher wages, they are also associated with lower employment in occupations with high exposure and low complementarity to AI, creating challenges for youth and some IT specialists (Jaumotte et al., 2026).
The literature highlights that common AI business applications include marketing automation, virtual agents and chatbots, natural language processing, and data and text analytics, alongside complementary investments in cloud services, storage, and workflow development (Bonney et al, 2024) Organizational adjustments supporting AI adoption frequently involve workforce training, redesigned workflows, cloud infrastructure, and improved data management practices (Bonney et al., 2024). Demand for AI skills such as artificial intelligence, machine learning, predictive analytics, generative AI, natural language processing, and Apache Spark spans education levels (Galeano et al, 2025), reinforcing interview insights that highlight the growing importance of technical foundations As one interviewee noted, “There’s a massive opportunity for Python knowledge and being able to build and deploy AI agents” (Bartos, 2025), Another emphasizes that “Only about five percent of our team needs to understand the backend, but everyone needs to understand data security and how AI fits into the system” (Kirchner, 2025) A third interviewee uncensored the breadth of required technical knowledge, stating that “People need to understand how programming languages work across applications Python, R, databases, cloud systems.” (Dicks, 2025).
Figure3.4.TopPostedSoftwareSkillsin ArtificialIntelligenceRelatedJobs
LESSONSLEARNED
Conclusion
This review highlights that while AI adoption is accelerating across industries, its impacts on jobs, skills, and career pathways remain complex and uneven Interview findings and much of the current literature suggest that AI is primarily augmenting work rather than replacing it, with organizations using AI to enhance productivity, streamline tasks, and support growth However, contrasting evidence points to emerging challenges, particularly for entry-level workers, younger cohorts, and occupations with high exposure to automation Given the rapid pace of adoption and the limited availability of longitudinal research, substantial uncertainty remains regarding long-term labor market impacts, underscoring the need for continued research and data-driven monitoring
Despite these uncertainties, clear and consistent themes emerge across interviews, labor market data, and the existing literature that point to actionable workforce implications These themes include rising expectations for AI literacy across education levels, the central importance of human skills such as critical thinking, ethical reasoning, and adaptability, and the growing need for both upskilling and reskilling as occupational roles evolve In response, the following Workforce Development Recommendations draw on practitioner insights, empirical evidence, and regional workforce data to support community colleges and education partners in preparing learners for an AI-enabled economy while remaining responsive to ongoing change and emerging research.
LESSONSLEARNED
Recommendations
1. Embed AI Literacy as a Core Workforce Competency
AI literacy should be treated as a foundational learning outcome across all programs, rather than a specialized or optional skill. Community colleges can integrate baseline AI instruction, similar to existing computer literacy requirements, into general education and workforce curricula. Instruction should introduce how AI systems function, responsible and ethical use, effective prompting, and techniques for verifying AI-generated outputs.
2. Prioritize Critical Thinking, Ethical Reasoning & Fact-Checking Skills
Critical thinking, ethical reasoning, and fact-checking are essential competencies in AI-enabled workplaces and should be central to instructional design Institutions may emphasize these skills through curriculum that strengthens analytical reasoning and ethical decision-making in AI-supported environments Scenariobased learning, where students evaluate and validate AI outputs, can help reinforce these competencies while ensuring employability skills remain a core component of workforce education
3. Develop Training Pathways for Both Upskilling and Reskilling Colleges should design flexible training pathways that support both upskilling and reskilling. Upskilling programs can help incumbent workers integrate AI into their existing roles, while reskilling pathways can support learners transitioning into new occupations as automation reshapes job demand.
4. Strengthen Technical Foundations and Cross Functional Digital Skills
Foundational digital skills should be scaffolded across programs to ort a wide range of learners Community colleges can offer d skill pathways, beginning with cloud fundamentals, privacy security awareness, device literacy, and navigation across al platforms Intermediate and advanced coursework can build rd Python, SQL, AI workflows, cybersecurity, automation ms, and edge-to-cloud architectures
courage a Culture of Curiosity and Experimentation
ational environments should foster curiosity, experimentation, confidence in using AI tools Colleges can provide lab and simulation learning environments where students safely explore AI applications without fear of failure Microlearning opportunities and low-stakes challenges can encourage experimentation, while instructional practices that reward creativity and initiative help students build adaptability and resilience.
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